The American Community Survey and Intercensal Population Estimates:
Where Are The Crossroads?

Amy Symens Smith

Population Division
U.S. Bureau of the Census
Washington, D.C. 20233-8800

December 1998

POPULATION DIVISION TECHNICAL WORKING PAPER NO. 31

An earlier version of this paper was presented at the American
Community Survey Symposium, Census Bureau, Suitland, MD, March 25,
1998 and at the annual meeting of the Population Association of
America, Chicago, IL, April 2-4, 1998.

This paper reports the results of research and analysis undertaken
by Census Bureau staff. It has undergone a more limited review
than official Census Bureau publications. This report is released
to inform interested parties of research and to encourage
discussion.

ABSTRACT

The Bureau of the Census is expanding two of its primary programs
-- the sample survey measurement program and the intercensal
population estimates program. On the survey front, testing for
the American Community Survey (ACS) is currently underway.
Intercensal estimates activities include the production of annual
population estimates of counties by age, sex, race and Hispanic
origin. Traditionally, the Census Bureau integrates survey
results with estimates by introducing the population estimates as
independent controls to the sample survey results.

This paper identifies many of the issues involved in integrating
survey results and population estimates, including methodological
differences in: (1) temporal concepts; (2) residence concepts;
and, (3) race and ethnic definitions. This paper also examines
concrete examples of traditional weighting issues. Using results
from the four 1996 ACS test sites, this paper compares the first
stage ACS data to independent population estimates, yielding
useful insights to the degree of ACS weighting needed. This paper
concludes by summarizing the crossroads between the ACS and
intercensal population estimates, and discusses some alternatives
and enhancements to the integration of these two programs.

I. INTRODUCTION

In response to the growing demands for current, continuous, and
timely demographic measures for small areas, the Bureau of the
Census is expanding two of its primary programs--the sample survey
measurement program and the intercensal population estimates
program. Presently, testing is underway for the American
Community Survey (ACS), a monthly household survey designed to
provide continuous demographic characteristics for counties,
places, and other small political areas. Recent intercensal
population estimates program activities include the addition of
annual population estimates of counties with age, sex, race, and
Hispanic origin detail.

Traditionally, the Census Bureau integrates survey results with
estimates by introducing the population estimates as independent
controls to the sample survey results. Many of the Census Bureau
surveys, for instance the Current Population Survey (CPS) and the
Survey of Income and Program Participation (SIPP), are controlled
to match independently derived intercensal population estimates.

These recent activities introduce a number of significant
challenges and issues for both Census Bureau programs. This paper
will identify many of the issues involved in integrating the ACS
data and intercensal population estimates considering the existing
methodological differences in: (1) temporal concepts; (2)
residence concepts; and, (3) race and ethnic definitions.

This paper will examine concrete examples of traditional weighting
issues. Using results of the 1996 test of the ACS conducted in
Multnomah County, OR; Rockland County, NY; Brevard County, FL;
and, Fulton County, PA, this paper will compare the first stage
ACS data to independent population estimates. Examining results
first for the total population, and then dissagregated by age,
sex, race, and Hispanic origin characteristics may yield useful
insights to the degree of weighting needed in the ACS data.

Finally, this paper will conclude by discussing some alternatives
and enhancements to the integration of the American Community
Survey with the intercensal population estimates program.

This paper is organized into a number of sections:

Section II -Provides an overview of the design,
implementation, and goals of the American Community Survey.

Section III -Summarizes the methodology used to
produce the 1996 county estimates with age, sex, race, and
Hispanic origin detail.

Section IV -Describes the procedure of
controlling the ACS results to the independent population
estimates. To illustrate this procedure Table 1 presents the ACS
results after both weighting stages compared to the population
estimates.

Section VI - Presents tables comparing the ACS
results and population estimates for each county with age, sex,
race, and Hispanic origin detail.

Section VII - Summarizes the findings and closes
with a discussion of the crossroads, alternatives, and
enhancements to the integration of the ACS and population
estimates.

II. THE AMERICAN COMMUNTY SURVEY

Continuous Measurement
As the newest development in the sample survey measurement
program, the ACS is part of the Continuous Measurement System.
The general idea for a program of "continuous measurement" goes
back to the suggestion for an "annual sample survey" proposed by
Census Bureau employee Philip Hauser in 1941. In the late 1980s
suggestions for "continuous measurement" resurfaced and this time
were incorporated as one of the options researched by the 2000
Census Research Staff. In its earliest stages there were no
design details, only a general idea to replace the long form with
an intercensal data collection program "spread out in some fashion
over the decade." 1 Today, the Census Bureau describes
Continuous Measurement (CM) as the reengineering of the method for
collecting the housing and socioeconomic data traditionally
obtained from the decennial census "long form." The Continuous
Measurement program includes the use of the ACS and intercensal
estimates. 2

Implementation
The American Community Survey testing is currently underway,
implementation is being conducted in four phases:

demonstration period 1996-1998

comparison sites 1999-2001

national comparison sample 2000-2002

full implementation 2003 and beyond

Once testing is complete, the American Community Survey is
designed to produce estimates of housing, social, and economic
characteristics every year for all states, as well as for all
cities, counties, metropolitan areas, and population groups of
65,000 persons or more. For smaller areas, it will take
two-to-five years to sample the same number of households as
sampled in the decennial census. For example, in rural areas,
city neighborhoods, or in places with population groups of less
than 15,000, it will take five years to accumulate a sample the
size of the decennial census. Eventually, the multi-year
estimates of characteristics will be updated annually for every
government unit, for components of the population, and for census
tracts and block groups. The ACS is proposed to replace the
census "long form" in the 2010 decennial census.

The 1996 American Community Survey
Results from the 1996 ACS represent the focus of this research.
The scope of the ACS in this phase was limited to housing units
(e.g. excluding group quarters populations), either occupied or
vacant, in four sites:

Brevard County, Florida

A single county Metropolitan Statistical Area (MSA)

Multnomah County, Oregon

A large metropolitan county that is part of a multiple county
PMSA. Available data is organized by county and entire site. The
entire site includes all of Portland city which is located
primarily in Multnomah County but also extends into Washington and
Clackamas Counties. Vice versa, data for the county does not
include the parts of Portland city which are outside the Multnomah
county borders. 3

Rockland County, New York

A metropolitan county that is part of the New York, NY Primary
Metropolitan Statistical Area (PMSA)

Fulton County, Pennsylvania

A nonmetropolitan county that does not have a county-wide
address system at the present time, and does not plan to convert
to a county-wide address system in the next few years.

Responding to inquires concerning how the four test sites were
chosen, the Census Bureau stated that they looked for sites that
addressed critical operational issues and met the evaluation
objectives of the demonstration period. Geographic balance was
sited as an important consideration so that each of the Regional
Offices would acquire experience with the ACS. The Census Bureau
considered the current survey workload in a particular site to see
if existing field representatives could be used rather than having
to hire new ones. The percentage of city-style addresses in
potential sites as well as the percentage of post office boxes,
and the sites' land area, were also considered. Finally, the
availability of local experts who were willing to use the data and
assist the Census Bureau in comparing the ACS and Census 2000 data
for each site was a factor. 4

Comparison Sites
In 1999-2001, the number of county sites in the sample will be
increased to thirty-seven comparison sites and eight phase-in
sites in 1999 only. The purpose of this comparison phase is to
collect several kinds of information via the ACS to understand the
differences between the 1999-2001 ACS and the 2000 census long
form. Comparison counties have been identified which are believed
to include various situations in which differences may be
prominent. To explain, sites were selected to have at least one
location in each of twenty-four strata representing combinations
of county population counts, difficulty of enumeration, and
1990-1995 population growth. The selection also attempted to
balance areas by region of the country, and sought to include
several sites representing different characteristics of interest
such as:

racial or ethnic groups

highly seasonal populations

migrant workers

American Indian reservations

improving or worsening economic conditions

predominant occupation or industry types.

The purpose of the comparison counties is to give a good
tract-by-tract comparison between the 1999-2001 ACS cumulated
estimates and the Census 2000 long-form estimates, and to use
these comparisons to identify both the causes of differences and
"diagnostic variables" that tend to predict certain kinds of
differences.

National Comparison Sample
In 2000-2002, pending Congressional approval of funding, plans are
to add a national sample of 700,000 housing units per year to the
ACS. This will allow the Census Bureau to provide estimates for
all states and for geographic areas or population groups of
250,000 persons or more. From the national sample, it will be
possible to deliver direct comparison information to show how data
from the American Community Survey compare with the data from the
census long form for all states, large cities, and large sub-state
areas. For areas with fewer people, such as small counties, small
towns, or census tracts, statistical modeling will give indirect
information telling how the ACS would typically compare to the
census long form "for an area like this." The model-based
comparison will use information from both the national sample and
the comparison counties, rather than just from the sample from
each small area.

Full Implementation
Finally, in 2003 the American Community Survey will be implemented
in every county of the United States with an annual sample of
three million housing units. Once the survey is in full
operation, ACS data will be available every year for area and
population groups of 65,000 or more beginning in 2004. For small
areas and population groups of 15,000 or less, it will take five
years to accumulate information to provide accurate estimates,
meaning that updated information for areas such as neighborhoods
will be available starting in 2008 and every year
thereafter. 5

The American Community Survey meets the needs of data users for
timely data that provide consistent measures for all areas. For
many geographic areas decennial sample data are out-of-date almost
as soon as they are published, about two years after the census is
taken, and their usefulness declines every year thereafter. Yet,
billions of government and business dollars are divided among
jurisdictions and population groups each year based on their
social and economic profiles in the decennial census. The
American Community Survey can solve this problem as it is
formulated to identify rapid changes in an area's population and
give an up-to-date statistical picture when data users need it, as
opposed to once every decade. Some of the proposed applications
for ACS data include the ability to: track the well-being of
children, families and the elderly; determine where to locate new
highways, schools, and hospitals; show a large corporation that a
town has the workforce the company needs; evaluate programs such
as welfare and workforce diversification; and, monitor and
publicize program results. For more information about the
American Community Survey visit the ACS website at
https://www.census.gov/cms/www/

III. POPULATION ESTIMATES PROGRAM

The Census Bureau estimates program, on the other hand, produces
population estimates for the nation, states, counties, and places
as part of its ongoing program to quantify changes in the
population size and distribution since the last census. Central
to the purpose of this research are the population estimates
produced for counties. The recent release of 1996 county level
population estimates with age, sex, race, and Hispanic origin
detail are part of a newly developed project which is now in an
intermediate stage. Below I provide an outline of the methodology
used to derive the intercensal population estimates.

County Estimates Methodology
Most recently the Census Bureau has released estimates of the
resident population of the counties in the United States by age
(ages 0 to 84; 85 and over), sex (male; female), race (White;
Black; American Indian, Eskimo and Aleut; Asian and Pacific
Islander), and Hispanic origin (Hispanic or non-Hispanic) for July
1st of each year from 1990 to 1996. These estimates are
consistent with:

the estimates of the population of States by age, sex, race,
and Hispanic origin; July 1st, 1990 to 1996

the July 1st, 1990 to 1996 postcensal estimates of the total
population of counties.

The county estimates are developed in a two-step process. First,
state level estimates with age, sex, race, and Hispanic origin
detail are developed using the cohort-component method whereby
each component of population change -births, deaths, domestic
migration, and international migration is estimated separately for
each birth cohort by sex, race, and Hispanic origin. The
cohort-component method is based on the traditional demographic
accounting system:

P1=P0 + B - D + NDM + NMA where:

P1= population at the end of the period
P0 = population at the beginning of the period
B = births during the period
D = deaths during the period
NDM = net domestic migration during the period
NMA = net migration from abroad during the period. 6

In the second stage, county estimates are then derived from the
state estimates using a two-step mathematical technique commonly
referred to as the ratio method. The 1990 census data for each
county by age, sex, race, and Hispanic origin are the starting
point. First, these 1990 data are aggregated to county totals and
adjusted to agree with the updated (in this case 1996) county
estimates. Second, the adjusted county data by age, sex, race,
and Hispanic origin are aggregated to state totals by age, sex,
race, and Hispanic origin and adjusted to agree with the state
estimates for these groups. Applying the ratio method in this
manner is often referred to as "raking" the data.

An additional refinement added to the production of this set of
estimates, is the separate estimation of the group quarters (GQ)
population. The GQ population are those individuals residing in
non-standard living arrangements such as correctional institutions
and nursing homes. Their demographic characteristics are often
very different from the rest of the population of the county in
which they reside, which is why it is useful to estimate them
separately. Unless otherwise noted, any county population
estimates incorporated in this paper will not include the group
quarters population. 7

This summary is meant to serve only as a brief description of the
recent developments in the sample survey program and the
intercensal estimates program. Additional information can be
gleaned from the Census Bureau's population estimates page at
https://www.census.gov/population/www/estimates/popest.html.

An overview of both the ACS and population estimates is necessary
before proceeding to the next section which provides a description
of how and why these two programs are integrated.

IV. INTEGRATION: POPULATION ESTIMATES AS ACS
CONTROLS

One of the most obvious ACS and population estimates crossroads is
the Census Bureau's tradition of introducing population estimates
as controls to sample survey results. This process is often
referred to as "calibrating," "weighting" or, in this case
ccontrolling," the ACS results for counties to independently
derived county population estimates. In addition to the ACS, many
other Census Bureau surveys, for instance the Current Population
Survey (CPS) and the Survey of Income and Program Participation
(SIPP), are calibrated to national population estimates by age,
sex, race, and Hispanic origin and to estimates of the population
aged sixteen and over for states and New York City and Los Angeles.

The process of weighting the ACS results is complex, only a brief
summary is provided here. Two sets of weights were assigned: (1)
a weight to each sample housing unit record; and, (2) a weight to
each sample person record. Estimates of person characteristics
are based on the person weight; estimates of family, household or
housing unit characteristics are based on the housing unit weight.
Characteristic estimates are made by summing the weights assigned
to the persons, households, and families or housing units
possessing the characteristics in the tabulation area. Initially,
each person in an occupied housing unit received the housing unit
weight as their person weight, at this point everyone in the
household had the same weight. Person weights were then
individually adjusted based on each persons' age, sex, race, and
Hispanic origin to match county population controls by age, sex,
race, and Hispanic origin independently derived by the Population
Estimates program. The estimation procedure used to assign the
weights was performed independently for each of the 1996 ACS
sites. 8

It is the aim of this paper to explore concrete examples of
traditional weighting issues. Comparing the ACS results and
population estimates may yield useful insight to the degree of
weighting needed in the ACS data (see Table 1). To ascertain the
degree of weighting needed an index of the ratio of the ACS
results (after housing unit weighting but before second stage
weighting ) to the population estimates is calculated. A value
over 100 indicates that the population estimate is higher than the
ACS estimate; conversely, a value under 100 indicates that the ACS
estimate is higher than the population estimate.

Indexes over one hundred consistently show that the ACS housing
weighted estimates are below the population estimates (see Table
1). Ruling out the possibility of sampling error as the primary
explanation, one possibility may be that the universe of housing
units the ACS results are weighted to in the first stage does not
represent the true universe of housing units in each county. Two
factors may explain why this is the case. First, the county
Master Address File (MAF) from which the ACS sample was selected
may not represent the true universe of units which actually exist
in each county. Second, the MAF may represent the true universe of
units, but people within the units were unknowingly missed; this
outcome may suggest response bias resulting in under or over
coverage and will be addressed later.

Each month a sample was selected from the national Master Address
File (MAF). The MAF was initially constructed by a computer match
of the U.S. Postal Service (USPS), Delivery Sequence File (DSF),
the 1990 Census Address Control File (ACF), and the Topologically
Integrated Geographic Encoding and Referencing (TIGER) files. The
MAF can be created in an automated fashion for all areas that have
a city-style address system where the mail is delivered using
these addresses. For areas that do not have a city-style address
system, such as Fulton county, the Census Bureau will create a MAF
by conducting an address listing operation.

While no statistical testing has been undertaken, the wide
variability in indexes between counties (101-106, respectively)
may indicate that a combination of the above explanations is
appropriate (see Table 1). In other words, in each county,
housing units may have been missed, as well as, persons within
captured housing units may have inadvertently been missed.
Another explanation may be that the quality of the MAF varies by
locale. Additionally, it is important to recognize that thus far
this analysis has assumed that the population estimates represent
"truth" for each county, which is clearly a dangerous assumption.
Below, ACS results and population estimates with age, sex, race,
and Hispanic origin will be compared county by county. Perhaps
these characteristics will suggest explanations, in addition to
those outlined here, which will account for this disparity.

In this paper Fulton county is considered to be a special case.
Due to its size, and the fact that no county-wide address system
exists, it may be dangerous to place a high level of confidence on
the ACS results. Throughout, Fulton county data will only be
presented for the reader's information.

Table 1.

First and second stage weighted 1996 ACS
results1 and 1996 population
estimates2 by county: Total population

County

American Community Survey

Population Estimate

Index3

Housing Unit Record Weighting

Person Record Weighting

Brevard

429,104

447,597

447,597

104.3

Multnomah

603,233

611,040

611,040

101.3

Rockland

264,031

270,962

270,962

102.6

Fulton

13,515

14,358

14,358

106.2

1ACS results do not include group quarters
population2population estimates do not include group quarters
population3index is calculated as (population estimate/ACS
housing unit record weighted)*100

V. INTEGRATION ISSUES AND CHALLENGES

A number of challenges and issues have accompanied the Bureau's
expansion of both the sample survey measurement program and the
intercensal population estimates program. Specifically,
integration of the ACS results and independent population
estimates reveal methodological differences pertaining to: (1)
residence concepts; (2) temporal concepts; and, (3) race and
ethnic definitions. Since the primary purpose of this paper is to
assess the comparability of the ACS data and intercensal
population estimates it is important to highlight these
differences now.

Residence Concepts
First consider residence concepts as they are defined in each data
source. The ACS survey as it was implemented in the four 1996
test sites relies on a "current residence" concept. Although very
close to a pure de facto, "who slept here last night" concept,
there is one main difference; the ACS aims to include everyone who
is currently living or staying at a unit, with the exception of
people who "usually" or "really" live somewhere else (e.g. their
de jure residence) and are gone from that usual residence for two
months or less. To introduce consistency in the assignment of
persons to a residence the ACS established the "two-month" rule.

The ACS two-month rule defines the current residence of all
persons, with only three apparent exceptions. First, children
below the college level away at school are considered residents of
their parental home. College students' current residence is
established by the two-month rule. Second, children who live
under joint custody agreements and move often between the separate
residences of their parents are considered to be current residents
of the sample unit if they are staying there when the contact with
the unit is made. Finally, in the instance of commuter workers,
persons who stay in a residence close to their work and return
regularly to another residence, usually with family, are
considered to be current residents of the family residence, not
the work-related one. 9

On the other hand, intercensal population estimates rely on the
residence concept of the decennial census, where persons are
required to have a de jure "usual residence." This is defined as
the place they live and sleep most of the time or the place they
consider to be their usual home. Although implied, but never
explicitly stated on the census form, "usual residence" is assumed
to be the place a person spends six months or more of the
year. 10 Since the 1950 census, college students have
been enumerated at the place of their college rather than where
their parents live and where they may return to during holidays
and summer. 11

For the majority of the population, their de facto and de jure
residence will be the same. There are, however, certain segments
of the population where the de facto and the de jure residence
will be different. Most notable are the "snowbirds." A snowbird
is a generic label for a person who lives in one place for an
extended portion of the year and another for the remainder. For
all intents and purposes a snowbird is a person who resides at a
seasonal residence for at least some portion of the year. The
decennial census has typically handled the "snowbird situation" by
determining which residence is the "usual" address as of the
census date and assigning the household to that location, even if
they are staying at the "nonusual" location at the time of
contact. The ACS, on the other hand, will count the snowbirds
where they are found.

Although the number of "snowbirds" is not great, the phenomenon is
geographically specific and presents a challenge in integrating
ACS and population estimates at the county level. The ACS does
recognize that appreciable differences may exist for areas where
large numbers of people spend several months of the year in units
that are not their primary residence, for example Florida,
Arizona, and in beach or mountain vacation areas. A working group
within the Population Division at the Census Bureau is currently
investigating this possibility.

Temporal Concepts
Another methodological variation between the ACS and intercensal
population estimates that warrants discussion is the reference
period applied to characteristics. To illustrate, the ACS annual
survey results constitute "average" annual estimates. On the
other hand, intercensal population estimates are based on the
characteristics at one particular point in time.

The annual ACS results represent the cumulative results of the
twelve month interview cycle. For areas with a seasonal
component, variations are included in the annual ACS picture. The
intercensal population estimates represent a picture at one point
in time. Although the intercensal estimates uses July 1st as its
reference point the snapshot date is not exact.

Going back to the estimates methodology, the intercensal estimates
begin with the decennial census population. For the county totals
and state estimates the annual components of population change are
added and subtracted. Because the population estimates are
heavily grounded to the decennial census the estimate begins with
the snapshot of the usual residence on census day, April 1st. For
the 1990 year, the population is moved forward to July 1st using
estimates of the components for the April-July period. Once the
population is moved forward to July 1st, the remaining subnational
estimates are estimated for each July 1st date using annual
estimates of the components of population change.

Although the components of birth, death and international
migration represent the actual July to July period, the estimates
of internal migration are not as neatly tied to the actual July
1st to July 1st period. Because the internal migration component
is an estimate heavily based on tax return information received
from January through August the reference period is not as
straight forward. However, in the intercensal estimates
environment we assume these annual estimates of internal migration
apply to the July 1st to July 1st period.

In sum, likening the ACS to a video and intercensal estimates to a
still photograph simplifies the explanation of reference period
differences. The ACS results can be thought of as a video that
runs over the course of a twelve month period which displays the
demographic, social, and household characteristics of the
population. As a sample survey running in different phases, the
ACS results are an "average" of the population's characteristics
over a twelve month period. Intercensal estimates, on the other
hand, are analogues to a still picture taken at one particular
point in time, July 1st. In theory the estimates derived by both
sources should be similar, both display characteristics of the
population over approximately the same period in the past. The
purpose of this paper is to test this relationship using the 1996
ACS test site data and the 1996 intercensal population estimates.

Race and Ethnicity Definitions
Finally, understanding the crossroads in terms of the definitions
of racial and ethnic characteristics is necessary to the purpose
of this research. Examination of both data sources reveal
similarities as well as differences in definitions.

In general, the racial and ethnic definitions utilized by the
Bureau of the Census reflect self-identification, and therefore
cannot be assumed to represent any clear-cut scientific definition
of biological stock. The data represent self-classification by
people according to the categories with which they most closely
identify. On the decennial census form, and during sample
surveys, persons are instructed to select the one response
category which best describes their racial identity and ethnic
origin group. The Census Bureau recognizes that the categories
include both racial and national origin or socio-cultural groups.

Categories

The racial and ethnic category classifications used by the Census
Bureau generally adhere to the "Race and Ethnic Standards for
Federal Statistics and Administrative Reporting" set forth in
Statistical Policy Directive No. 15, issued by the Office of
Management and Budget (OMB). This Directive provides common
language, and promotes uniformity and comparability of race and
ethnicity data. 12 The minimum race and ethnic
categories designated by OMB are:

White includes persons who indicated their race
as "White" or reported entries such as Canadian, German, Italian,
Lebanese, Near Easterner, Arab, or Polish

Black includes persons who indicated their races
as "Black or Negro" or reported entries such as African American,
Afro-American, Black Puerto Rican, Jamaican, Nigerian, West Indian
or Haitian.

American Indian, Eskimo or Aleut includes persons
who classified themselves as:

American Indian includes persons who indicated
their race as "American Indian," entered the name of an Indian
tribe, or reported such entries as Canadian Indian,
French-American Indian, or Spanish-American Indian.

Information on tribe is based on self-identification and therefore
does not reflect enrollment in or any designation of Federally- or
State-recognized tribe.

Eskimos includes persons who indicated their race
as "Eskimo" or reported entries such as Arctic Slope, Inupiat, and
Yupik.

Aleut includes persons who indicated their race
as "Aleut" or reported entries such as Alutiiq, Egegik, and
Pribilovian.

Asian and Pacific Islander includes persons who
reported in one of the Asian or Pacific Islander groups listed on
the questionnaire or who provided write-in responses such as Thai,
Nepali, or Tongan.

Chinese includes persons who indicated their race
as "Chinese" or who identified themselves as Cantonese, Tibetan,
or Chinese American. In standard census reports, persons who
reported as "Taiwanese" or "Formosan" are included here with
Chinese.

Filipino includes persons who indicated their
race as "Filipino" or reported entries such as Philipino,
Philipine, or Filipino American.

Japanese includes persons who indicated their
race as "Japanese" and persons who identified themselves as
Nipponese or Japanese American.

Asian Indian includes persons who indicated their
race as "Asian Indian" and persons who identified themselves as
Bengalese, Bharat, Dravidian, East Indian or Goanese.

Korean includes persons who indicated their race
as "Korean" and persons who identified themselves as Korean
American.

Vietnamese includes persons who indicated their
race as "Vietnamese" and persons who identified themselves as
Vietnamese American.

Other Asian includes persons who provided a
write-in response such as Bangladeshi, Cambodian, Indonesian,
Laotian, Pakistani, Sri Lankan, Amerasian, or Eurosian.

Pacific Islander includes persons who indicated
their race as "Pacific Islander" by classifying themselves into
one of the following groups or identifying themselves as one of
the Pacific Islander cultural groups of Polynesian, Micronesian,
or Melanesian.

Hawaiian includes persons who indicated their
race as "Hawaiian" as well as persons who identified themselves as
Part Hawaiian or Native Hawaiian.

Samoan includes persons who indicated their race
as "Samoan" or persons who identified themselves as Chamorro or
Guam.

Guamanian includes persons who identified their
race as "Guamanian" or persons who identified themselves as
Chamorro or Guam.

Other race includes all other persons not
included in the "White," "Black," "American Indian, Eskimo, or
Aleut," and the "Asian or Pacific Islander" race categories
described above.

The above outlined race categories are mainly consistent across
both the 1990 decennial census form and the questionnaire used in
the four 1996 test sites. The ACS questionnaire had one important
difference. In addition to the racial categories included in the
cecennial census, respondents in the ACS were given the
opportunity to designate themselves as "multiracial." Multiracial
persons where also asked to supply a write-in response, similar to
the procedure for choosing "other" race. 13

Modified-Age-Race-Sex
Although the intercensal estimates are strongly tied to the
decennial data, for the intercensal population estimates program
some modifications were introduced to the age and race data. The
construction of the modified age, race, sex (MARS) file was
necessary to ensure comparability to other data sources. From the
decennial census data the race statistics were collapsed into four
categories (white; black; American Indian, Eskimo and Aleut; and,
Asian and Pacific Islander). The two ethnicity groups remain
constant, Hispanic and non-Hispanic. The age data were modified
to correspond with the April 1, 1990 census date. Overall the
"modified" data counts remain consistent with the 1990 counts of
the census as enumerated, and are used as the base to construct
annual intercensal population estimates. The following paragraphs
describe the construction and demand for this file.

Race modification
In the 1990 census there were just under ten million reports of
"other race" which needed to be assigned to one of the four race
categories when collapsed in the MARS file. Indicating other race
meant that these people were not included in one of the fifteen
racial categories listed on the census form and therefore could
not be collapsed into one of the four categories. The existence
of this group is inconsistent with the race categories defined by
OMB in Directive No. 15. Such "non-specified" race persons are
not found in data sources other than the census. In order to
serve the needs of some portions of the user community, and to
construct intercensal estimates, it is necessary to assign each of
these persons to a specified race. The methods for doing this in
the MARS file are outlined below.

After evaluating many alternatives, the following race assignment
rule was used, namely to assign each "other race" person to the
specified race reported by a nearby person with an identical
response to the Hispanic origin question. Specifications of this
Race Assignment rule include:

First, that the specific Hispanic origin of each "other race"
person in the 1990 census was taken into account when assigning
them to a specified race. This was considered appropriate because
over ninety-five percent of the "other race" persons were of
Hispanic origin. Their Hispanic origin response was used, whether
or not it had been allocated, in order to preserve the race
distribution within each type of origin. The specific Hispanic
origin responses were "not Spanish/Hispanic," "Mexican," "Puerto
Rican," "Cuban," and "other Spanish/Hispanic."

Second, virtually every person who reported both a specified race
and an origin was included in the "donor pool" of eligible
persons. The sole exception was the exclusion of several
non-specific American Indian codes from the donor pool since: (1)
preliminary 1990 research suggested questionable reporting in the
American Indian category; and, (2) previous research showed that a
high proportion of such persons were much less likely to be
American Indians than those who actually provided a specific tribe
response as instructed on the census form. These codes were:
548-American White; 549-American Black; 597 American Indian (no
tribe reported); 598-American Indian (tribal responses not
elsewhere classified), and 973-FOSDIC circle with no write-in
response. These were excluded because of evidence from the 1980
census that misreporting of race was much higher in these codes
than it was in codes representing specific American Indian tribes.
Consistent with advisory committee recommendations, any person
assigned to the American Indian race through allocation was give
code 973 rather than a specific tribal code.

Third, the assignment of a specified race was made on an
individual basis. That is, no effort was made to minimize racial
heterogeneity within households. Any such attempt would have made
it difficult to assign race in a manner which approximated the
specified-race distribution reported by persons with the same
Hispanic origin response.

Fourth, the race, origin, or sex of some persons also changed as a
result of the assignment of a different age to them during the
application of the age modification procedures. Their changed age
sometimes caused the person to be allocated a different
relationship and/or sex which resulted in the person receiving
their race or origin from a different person in the household,
since those items were allocated according to a hierarchy of
relationships.

Fifth, the results of the race modification procedures were
overridden in four counties where the American Indian population
grew by more than 100 percent and also became at least one
percentage point more of the county's population: Adams county,
WA; Harmon county, OK; Clark county, and Washington county,
ID.

Finally, in most census allocations procedures, acceptable data
from eligible persons (donors) are far more common than are the
cases where the value is assigned to persons without the
characteristics (the donees). This means information from any
given donor is rarely used more than once. Such large
donor-to-donee ratios were not unusual here. However, there were
a number of occasions where those needing a specified race
outnumbered those who reported the same origin as well as a
specified race. 14

ACS
As mentioned above, the ACS racial and ethnic definitions match
those used in the decennial census, except for the addition of a
"multiracial" category. Particularly since the 1990 census, the
OMB standards have come under increasing criticism from those who
believe that the minimum racial and ethnic categories set forth in
Directive No. 15 do not reflect the increasing diversity of our
Nation's population resulting primarily from growth in immigration
and interracial marriages. Four years ago OMB initiated a
thorough review of Directive No. 15 for possible revisions. As
part of the review, the Bureau of the Census tested a
"multiracial" race category in several surveys, including the ACS.
Thus, race categories in the final ACS products are slightly
different than those in the decennial census and include the
general categories "White," "Black," "American Indian, Eskimo, and
Aleut," and the "Asian and Pacific Islander," as well as, persons
who identified themselves as "some other race" or "multiracial."
Persons who identified themselves as "some other race" or
"multiracial" were provided with a write-in area where they could
be more specific. Written entries were reviewed, edited, and
coded by subject matter specialists. 15

In terms of comparability then, the 1990 decennial census had a
response category termed "other race." In the ACS this category
is instead termed "some other race." As part of the effort to
provide research useful to the OMB's review of Statistical
Directive No. 15, the ACS also included a "multiracial" category
on the race question. For weighting purposes we collapsed the
full ACS race categories into the common four categories, white,
black, AIEA and API to be consistent with the MARS file. For
simplicity, for weighting purposes only, we chose to collapse the
"some other race" and "multiracial" categories, in the ACS, with
the white category. Results may show that this technique will
need to be explored more fully in the future.

In sum, it is clear that there are important methodological
differences between the American Community Survey, as implemented
in the four 1996 test sites, and the intercensal population
estimates which rely largely on the 1990 decennial census. In
terms of temporal and residence rules the Census Bureau maintains
that few substantial variations should appear in the two data
sources as a result of these concept variations.

In addition to the differences in residence, temporal and
race/ethnicity concepts we need to remember that the ACS results
are products of sample surveys. Using sampling methodology the
data in the ACS products are estimates of the actual figures that
would have been obtained by interviewing the entire population.
The estimates from the chosen sample also differ from other
samples of housing units and persons within those housing units.
The possibility of sampling errors arise due to the use of
probability sampling, which is necessary to ensure the integrity
and representativeness of sample survey results.

In addition to sampling error, other types of errors may appear
during any of the various complex operations used to collect and
process survey data. For example, operations such as editing,
reviewing, or keying data from questionnaires may introduce error
into the estimates. These and other sources of error contribute
to the nonsampling error component of the total error of survey
estimates. Nonsampling errors may affect the data in two ways.
Errors that are introduced randomly increase the variability of
the data. Systematic errors which are consistent in one direction
introduce bias into the results of a sample survey. The Census
Bureau protects against the effect of systematic errors on survey
estimates by conducting extensive research and evaluation programs
on sampling techniques, questionnaire design, and data collection
and processing procedures. In addition, an important goal of the
American Community Survey is to minimize the amount of nonsampling
error through nonresponse for sample housing units. One way of
accomplishing this is by following-up on mail nonrespondents
during the CATI and CAPI phases.

Standard error is a measure of the deviation of a sample estimate
from the average of all possible samples. Sampling errors and
some types of nonsampling errors are estimated by the standard
error. The sample estimate and its estimated standard error
permit the construction of interval estimates within a prescribed
confidence that the interval includes the average result of all
possible samples. Direct estimates of the standard errors were
calculated for all estimates which will be provided below. The
standard errors, in most cases, are calculated using standard
variance estimates software using a methodology that takes into
account the sample design and estimation procedures.

In the next section the question of comparability will be raised;
first stage weighted ACS results (after household weighting but
before person weighting) for the four 1996 test sites will be
compared to corresponding intercensal population estimates. Age,
sex, race, and Hispanic origin characteristics will be explored to
understand differences.

VI. RESULTS

Crossroads between the American Community Survey and intercensal
population estimates can best be understood by comparing actual
results. ACS results and intercensal population estimates by
county for the total population were compared above (see Table 1).
That analysis showed us the process of weighting, as well as the
total amount of weighting needed by locale. Now, the ACS results
and population estimates will be further dissagregated by age,
sex, race, and Hispanic origin. This analysis may provide insight
to the degree of weighting needed by characteristic, interweaving
the methodological differences discussed above.

Table 2 compares the 1996 ACS results and population estimates by
age (five year age groups) and gender. Although statistical
testing of ACS results and population estimates to establish
significant differences within counties has not yet been
undertaken, it is worthwhile to explore logical explanations for
these findings. It is believed that the majority of the
differences apparent in Table 2 can be described by the
non-response of certain groups in surveys and differences in
residence and temporal concepts.

Nonresponse bias may be an important factor in at least Brevard
and Rockland counties. Providing insight to this issue is
Chakrabarty's (1994) research exploring the effects of nonresponse
bias in the coverage of the April 1990 Current Population Survey
and the 1990 census. Chakrabarty's results indicate that:

Blacks had more undercoverage in the CPS relative to the
census than whites. Considerably more undercoverage was found for
Hispanics.

Females have better coverage in the CPS (versus the census)
vis-à-vis males, for whites, blacks and Hispanics of all ages
except 65+.

Black men 25-44 years old have very poor coverage in the CPS
compared to the coverage for their female counterparts.
Similarly, white men 25-44 years old have slightly lower coverage
than their female counterparts.

Returning to Table 2, and concentrating on the percent
distributions of the population by age, Brevard county shows
possible survey nonresponse for males at ages 20-24 (ACS 4.0% v.
estimate 5.3%); ages 25-29 (ACS 5.3% v. estimate 6.8%); and ages
30-34 (ACS 7.7% v. estimate 8.4%). Possible nonresponse is also
apparent for females age 20-24 (ACS 4.0% v. estimate 5.0%) and
ages 25-29 (ACS 5.0% v. estimate 6.4%). These findings are
consistent with the Chakrabarty's research in terms of age and
gender characteristics.

Although not to the same magnitude, nonresponse is perhaps also a
factor in Rockland county. For males ages 20-24 and 25-29 the ACS
estimates are below the population estimates. Results show that
similar problems do not arise for women. Part of Chakrabarty's
research suggests undercoverage for males, especially black males,
in MSAs (versus nonMSAs). As a metropolitan county that is part
of the New York, NY Primary Metropolitan Statistical Area this may
be an important factor.

The other phenomenon which may explain ACS and population estimate
differences, especially for the population sixty five and over,
are differences in methodologies as they pertain to residence and
temporal concepts. Table 2 shows that in Brevard county
differences in the percent distribution for persons sixty-five and
over is nearly three percent. Overrepresentation of women in the
ACS compared to the population estimate is also apparent beginning
even earlier than for men, at age sixty. Recognition of
differences in residence and temporal concepts becomes important
when trying to capture residents such as the elderly who may live
most of the year in one residence and winter in another. This
group was previously referred to as "snowbirds". Application of a
two month rule (ACS) versus a usual residence/6 month rule
(population estimates) may yield different results. Research
suggests that determinants of migration (either seasonal or
permanent) for the elderly population are noneconomic, primarily
focusing on the opportunity for recreational development and other
amenities, as well as mild temperatures (Heaton et al. 1981;
Mueser and Graves 1995; Murdock et al. 1984). Based on these
criterion Brevard county may be especially attractive to persons
sixty-five and over at least sometime during the year.

Similarly, higher percentages of persons sixty-five and over are
captured by the ACS in Rockland county. The percent differences
in the distribution for both males and females follow the same
patterns as in Brevard county. Clearly Rockland county, NY does
not fit the normal characteristics of a locality with amenities
and a mild temperature where a large segment of the elderly
population might want to winter. This finding cannot be easily
explained.

Table 3 compares the ACS results and population estimates by race.
In general, the coverage of all racial groups except blacks is
considered to be good. Survey undercoverage similar to that
described by Chakrabarty (1994) may be responsible for the smaller
percentages of blacks found in all counties except Rockland.

Unlike any other county, Rockland shows an ACS result for the
black population 1.1% higher than the population estimate.
Considering the research outlined above (Chakrabarty 1994), an
overcoverage of blacks is not typical. Salvo and
Dahl 16 have undertaken some research which helps to
explain this finding. Tabulations of the ACS data not included
here show that Rockland has relatively more persons that checked
the "other race" or "multiracial" checkbox and wrote in Caribbean
which were coded black. Considering that Rockland is a large
metropolitan area near New York City, it has been hypothesized
that perhaps these persons are immigrants from the Caribbean.
This is a likely hypothesis considering that the ACS does not have
a specific race code to classify these persons. If this is in
fact the case and these persons were classified as black they may
inflate the number of blacks in the survey relative to the
population estimate.

Table 3 shows the ACS "other" race category dissagregated for
those indicating they were "multiracial" and the remainder of the
category. In each county the percentage of the population in the
"other" category is very small, ranging from 2.2% in Rockland to
1.1% in Brevard. Again it is important to review the allocation
procedures applied to both the ACS results and the population
estimates and consider their impact on these distributions.

As discussed above, the population estimates use as their base the
MARS file. In this file the race statistics for all the possible
race categories were collapsed into four categories (white; black;
American Indian, Eskimo and Aleut; and, Asian and Pacific
Islander.) Each person that indicated "other" race in the 1990
census was reassigned a race after taking into consideration their
Hispanic origin. This procedure was designed so that based on the
person's Hispanic origin (either Hispanic or non-Hispanic) they
were reassigned the race of a person close to them. Ultimately
this procedure reassigned these persons' race to match the race
distribution of persons that reported their race, crossed by
Hispanic origin. Assigning a person a race based on their
closest neighbor is usually referred to as "hotdecking."

For weighting purposes only, the ACS data had to be collapsed into
the four racial categories. The allocation procedure used to
reassign persons indicating "other" race and "multiracial" was a
little bit different in the ACS. Examination of some of the
initial returns indicated that the percentage of the population
indicating "other" and "multiracial" was minimal. But, these
racial groups had to be reassigned to one of the four race groups
to ensure data comparability, namely with the population
estimates. For these reasons, all persons in the ACS "other" race
category were reassigned to the white race category.

Table 4 shows the crosstabulation of race and Hispanic origin.
The purpose of showing these data is to investigate the allocation
procedure applied to the ACS results. In Brevard and Multnomah
counties reassigning the other group to the white category instead
of initiating a technique similar to the "hotdecking" procedure
used in the MARS file would change the distribution little. The
reason for this is that the percentage of the population that is
white of either Hispanic or non-Hispanic origin is rather large.

The racial distribution indicates that Rockland county is slightly
more diverse than the other counties. Blacks and Asian and
Pacific Islanders represent a larger percentage of the racial
distribution than in any other county. Additionally, Rockland had
the largest percentage of individuals indicating "other" race
compared to any other county, this may be further evidence of
greater racial diversity. These findings may indicate that this
allocation procedure is doing "injustice" to the apparent racial
diversity in Rockland.

VII. CONCLUSIONS AND DISCUSSION

Implementation of the new American Community Survey along with
recent advancements in the population estimates program have
recently raised a significant number of challenges and issues for
the Census Bureau. The purpose of this paper was to address a
number of these challenges and issues through investigation of
methodology and analysis of actual data. In the end, the goal of
this paper is to discuss the crossroads between the ACS and the
population estimates program as a context for suggestions about
alternatives and enhancements for their integration.

Integration of the ACS and population estimates stems from the
Census Bureau's tradition of applying independently derived
population estimates as controls to sample survey data. In this
case, the ACS results, in the last stage of weighting, were
controlled to equal the population estimates. This procedures
relies on the notion that population estimates are the best source
of "truth" about population counts during intercensal periods.
With this assumption in mind, one aim of this paper was to examine
actual results from the 1996 ACS test sites compared to 1996
population estimates to draw conclusions about the need for weighting.

Results for the total population indicate that weighting is needed
across all counties to increase the ACS results (after household
weighting) to equal the population estimates. Some explanations
for the survey bias relate to the accuracy of the MAF and the
undercoverage of persons. The accuracy of the MAF is open to
question. Since the MAF is constructed from a number of sources
including information from the U.S. Postal Service (USPS), the
1990 Census Address Control File (ACF), and the Topologically
Integrated Geographic Encoding and Referencing (TIGER) files the
quality of the MAF is only as good as the weakest link. Response
bias is another possible explanation. Although the housing unit
may be included in the MAF and be sent a questionnaire we could
miss some persons in the housing unit. Not everyone will complete
or be included on the returned questionnaire.

The accuracy of the population estimates is another source of
possible differences. Since the analysis thus far is based on the
idea that the population estimates represent "truth" the idea that
the quality of the estimate may vary by locale has not be raised.
Taken together, it is believed that these explanations explain why
the ACS results need weighting. However, the population estimates
are subject to error. Results from a 1990 evaluation show that at
the end of a ten year estimation period there is an average error
of 3.6% in the county population estimates (see Davis).

Comparison of the ACS results and the population estimates with
age, sex, race and Hispanic origin detail provide insight to
weighting issues by characteristics. This analysis is essential
to ascertain whether the degree of weighting needed in the ACS
data differs by any of the characteristics. Data with age and sex
detail show that common nonresponse bias, as well as residence and
temporal differences applying to the population sixty and over,
account for the largest amount of ACS/population estimate
discrepancy.

Turning to the race and Hispanic origin characteristics, unusual
patterns are found for blacks. In Brevard and Multnomah counties
the undercoverage of blacks is consistent with previous research.
Rockland county shows the opposite; the ACS results show an
overcoverage of blacks compared to the population estimate. This
finding is related to differences in ACS and population estimate
racial definitions. This outcome may suggest the need for
definition comparability between the two.

The overrepresentation of blacks according to the ACS in Rockland
county introduces another issue. It is hypothesized that the
blacks overrepsented in the ACS are actually black immigrants from
the Caribbean. As a sample survey the ACS would capture these
persons as long as they immigrated into Rockland county over the
last twelve month period. The population estimate would capture
these persons slightly differently due to its methodology.
Remember that county population estimates by age, sex, race, and
Hispanic origin are developed with a ratio approach, calibrating
the initial 1990 age, sex, race distribution of the county to
updated count tabulations and state estimates derived with
components of change for age, sex, race, and Hispanic origin.
Thus, the influx of immigrants from the Caribbean to Rockland
county would affect the New York state estimates.

Information on migration flows at the state level are currently
provided through a project which uses administrative records
supplied by the Internal Revenue Service. State flows are
dissagregated to show net domestic and net international migration
by race and Hispanic origin. The Census Bureau is currently
exploring whether this data source can be used to supplement the
already existing county migration flows with race and Hispanic
origin characteristics. In this case the ACS data was able to
inform the population estimates about a migration flow with a
unique impact for the black population. This is one example of
how the ACS might enhance population estimates.

Also pertaining to Rockland county, another important discovery of
this research concerns the allocation procedures used to reassign
the race of persons which indicated themselves as "other" race or
"multiracial" on their ACS questionnaire. Reassignment of race to
one of the four racial categories (white; black; American Indian,
Eskimo, Aleut; Asian or Pacific Islander) is necessary for
comparability with the population estimates. As described above,
the population estimates use as their base the MARS file. A
"hotdecking" procedure reassigns persons to the race of another
person close to them based on the Hispanic origin response they
gave. Simply put, persons will be redistributed based on being
either Hispanic or non-Hispanic according to the racial
distribution which already exists. On the other hand, the ACS
allocation procedure used to reassign persons indicating "other"
race and "multiracial" was to put them all into the white category
again based on their Hispanic origin response, this is done for
weighting purposes. This procedure was designed based on the fact
that the number of persons is extremely small, and that in all
instances whites represent the majority category.

In Rockland, blacks and Asian and Pacific Islanders represent a
larger percentage of the racial distribution than in any other
county and the largest percentage of individuals indicating
"other" race compared to any other county. While the number
reassigned to white is small, the question arises concerning how
much "injustice," if any, is done to the other racial groups; it
is important to note that re-calculation is used for weighting
purposes only. However, remember the ACS also collected data on
the social characteristics of the people. If the number of
persons reassigned to white became large, the social
characteristics the survey reports may become distorted. The
integration of the ACS and population estimate, operating from
different methodologies, may suggest that instead of relying on
the allocation procedure currently used in the ACS a "hotdecking"
procedure similar to that used in the population estimates may be
appropriate.

Overall, this paper has succeeded in highlighting crossroads
between the ACS and population estimates as a result of their
integration by the Census Bureau. Examining methodologies and
weighting procedures, no major obstacles where found in the
roadway which connect the two. As integral components of the
Census Bureau's Continuous Measurement System both will continue
to enhance one another as the ACS adds comparison site in
1999-2001 and moves to full implementation in 2003 and beyond.

However, the above analysis does indicate a few bumps in the
roadway which connects the ACS and population estimates which the
Census Bureau may look to reconcile in the future. The ability of
the population estimates to reflect migration flows apparent in
the American Community Survey data may indicate one way the ACS
can enhance, at least the migration component, of the population
estimates. The future application of research currently being
conducted with administrative records such as the IRS data may
smooth some of these bumps.

Finally, since the ACS implemented a race questions similar to
that which will be used in the 2000 census, the results are a
wealth of information. The above analysis suggests some bumps in
the roadway due to the differential allocation of persons
indicating "other" race and in the case of the ACS "multiracial".
Currently, these differences appear to be minor, the percentage of
persons reassigned to white in the ACS is most likely not large
enough to distort the overall social characteristics by race.
However, if the number of persons classifying themselves in this
way were to grow this problem may expand from a bump to a major
potthole. These issues of reconciliation by race are important
and are a major topic for the preparation of population estimates
during the next year.

1U.S. Bureau of the Census. 1997. FAQs: Technical.
https://www.census.gov/CMS/www.index_d.htm2 U.S. Bureau of the Census. 1997. The Continuous Measurement
System. https://www.census.gov/CSM/www.index_d.htm3 U.S. Bureau of the Census. 1997. Overview of 1996 American
Community Survey. https://www.census.gov/CMS/www/index_b.htm4 U.S. Bureau of the Census. 1997. FAQ's: Technical. FAQ's:
Technical. https://www.census.gov/CMS/www.index_d.htm5 U.S. Bureau of the Census. 1997. About the ACS.
https://www.census.gov/CMS/www/index_a.htm6 Sink, Larry D. 1997. Estimates of the Population of States by
Age, Sex, Race, and Hispanic origin: 1990-1996.
https://www.census.gov/population/estimates/county/casrh_doc.txt7 Sink, Larry D. Estimates of the Population of Counties by
Age, Sex, Race, and Hispanic origin: 1990-1996.
https://www.census.gov/population/estimates/county/casrh_doc.txt8 U.S. Bureau of the Census. 1997. Accuracy of the Data.
https://www.census.gov/CMS/www/index_b.htm9 Memo 8/15/98. Stanley Rolark. Residence Rules for the
American Community Survey.10 U.S. Bureau of the Census. Differences Between the 1996
American Community Survey (ACS) and the Census 2000 Long Form Survey.11 U.S. Bureau of the Census. 1992. 1990 Census of the
Population: General Population Characteristics, Apendix D. Collection and
Processing Procedures. (1990 CP-1-1. Washington D.C.: U.S. Government
Printing Office.12 Office of Management and Budget. 1997. Federal
Register Part II: Revisions to the Standards for the
Classification of Federal Data on Race and Ethnicity; Notices13 U.S. Bureau of the Census 1997. Census Bureau ACS data
definitions - Race. http:/www/census/gov/CMS/www/index_b.htm
U.S. Bureau of the Census. 1997 Census Bureau ACS data definitions -
Hispanic Origin http:/www/census/gov/CMS/www/index_b.htm14 U.S. Bureau of the Census 1991. Age, Sex, Race, and Hispanic
Origin Information from the 1990 Census: A Comparison of Census Results
with Results Where Age and Race Have Been Modified. 1990 CPH-L-74.15 U.S. Bureau of the Census 1996. Race code list for 1996
American Community Survey16 Memo 6/4/97. Scot Dahl. Black Race Codes.

REFERENCES

Chakrabarty, Rameswar P. 1994. Coverage of the Current Population
Survey Relative to the 1990 Census. Unpublished paper from the
Statistical Research Division. U.S. Bureau of the Census.

Heaton, Tim, William Clifford and Glenn Fuiguitt. 1981. Temporal
Shifts in the Determinants of Young and Elderly Migration in
Nonmetropolitan Areas. Social Forces, 60(1) September:41-60

Hough, George and David A. Swanson. 1997. Toward an Assessment of
Small Area CM Data: An Initial Comparison of 1996 ACS Returns with
1990 Long Form Returns for Tracts in the Portland Test Site. Paper
presented in the invited session "The American Community Survey -
Uses and Issues" at the Annual Meeting of the American Statistical
Association, Anaheim, California.

Mueser, Peter and Philip Graves. 1995. Examining the Role of
Economic Opportunity and Amenities in Explaining Population
Redistribution. Journal of Urban Economics, 37:176-200.

Salvo, Joseph J. and Arun Peter Lobo. 1997. The American Community
Survey: Nonresponse Follow-Up In the Rockland County Test Site.
Paper presented at the Annual Meeting of the American Statistical
Association, Anaheim, California.